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| import streamlit.components.v1 as components | |
| from streamlit_player import st_player | |
| from transformers import pipeline | |
| from tabulate import tabulate | |
| import streamlit as st | |
| st.header("stream your emotions") | |
| st.caption("LOVE: i love you") | |
| st.caption("SURPRISE: shocking") | |
| st.caption("SADNESS: i feel exhausted") | |
| st.caption("JOY: bro i feel so energetic") | |
| st.caption("FEAR: im scared of what lies ahead") | |
| st.caption("ANGER: you piss me off") | |
| def tester(text): | |
| classifier = pipeline("sentiment-analysis", model='bhadresh-savani/distilbert-base-uncased-emotion') | |
| results = classifier(text) | |
| #st.subheader(results[0]['label']) | |
| #tester(emo) | |
| generator = st.button("Generate Song!") | |
| if (generator == True): | |
| st.subheader(results[0]['label']) | |
| if (results[0]['label']=="joy"): #songs for joy emotion | |
| with open('joyplaylist.txt') as f: | |
| contents = f.read() | |
| components.html(contents,width=560,height=325) | |
| elif (results[0]['label']=="fear"): | |
| with open('fearplaylist.txt') as f: | |
| contents = f.read() | |
| components.html(contents,width=560,height=325) | |
| elif (results[0]['label']=="anger"): #songs for anger emotion | |
| with open('angryplaylist.txt') as f: | |
| contents = f.read() | |
| components.html(contents,width=560,height=325) | |
| elif (results[0]['label']=="sadness"): #songs for sadness emotion | |
| with open('sadplaylist.txt') as f: | |
| contents = f.read() | |
| components.html(contents,width=560,height=325) | |
| elif (results[0]['label']=="surprise"): | |
| components.html("""<iframe width="560" height="315" src="https://www.youtube.com/embed/dQw4w9WgXcQ?autoplay=1" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>""",width=560,height=325) | |
| elif (results[0]['label']=="love"): | |
| with open('loveplaylist.txt') as f: | |
| contents = f.read() | |
| components.html(contents,width=560,height=325) | |
| emo = st.text_input("Enter a text/phrase/sentence. A corresponding song will be recommended based on its emotion.") | |
| st.sidebar.subheader("Description") | |
| st.sidebar.write("This application detects the emotion behind your text input and recommends a song that matches it.") | |
| st.sidebar.subheader("Disclaimer/Limitations") | |
| st.sidebar.write("The model only outputs sadness, joy, love, anger, fear, and surprise. With that said, it does not completely encompass the emotions that a human being feels, and the application only suggests a playlist based on the aforementioned emotions.") | |
| st.sidebar.subheader("Model Description") | |
| st.sidebar.write("This application uses the DistilBERT model, a distilled version of BERT. The BERT framework uses a bidirectional transformer that allows it to learn the context of a word based on the left and right of the word. According to a paper by V. Sanh, et al., DistilBERT can \"reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities, and being 60% faster.\" This is why the DistilBERT model was used. For more information about the paper, please check out this [link](https://arxiv.org/abs/1910.01108).") | |
| st.sidebar.write("The specific DistilBERT model used for this is Bhadresh Savani's [distilbert-base-uncased-emotion] (https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion). It is fine-tuned on the Emotion Dataset from Twitter, which can be found [here](https://huggingface.co/datasets/viewer/?dataset=emotion).") | |
| st.sidebar.subheader("Performance Benchmarks") | |
| st.sidebar.write("[Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)") | |
| st.sidebar.write("Accuracy = 93.8") | |
| st.sidebar.write("F1 Score = 93.79") | |
| st.sidebar.write("Test Sample per Second = 398.69") | |
| st.sidebar.write("[Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion)") | |
| st.sidebar.write("Accuracy = 94.05") | |
| st.sidebar.write("F1 Score = 94.06") | |
| st.sidebar.write("Test Sample per Second = 190.152") | |
| st.sidebar.write("[Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion)") | |
| st.sidebar.write("Accuracy = 93.95") | |
| st.sidebar.write("F1 Score = 93.97") | |
| st.sidebar.write("Test Sample per Second = 195.639") | |
| st.sidebar.write("[Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion)") | |
| st.sidebar.write("Accuracy = 93.6") | |
| st.sidebar.write("F1 Score = 93.65") | |
| st.sidebar.write("Test Sample per Second = 182.794") | |
| tester(emo) | |